August 18, 2020
PyTorch 1.6 now includes Stochastic Weight Averaging
Do you use stochastic gradient descent (SGD) or Adam? Regardless of the procedure you use to train your neural network, you can likely achieve significantly better generalization at virtually no additional cost with a simple new technique now natively supported in PyTorch 1.6, Stochastic Weight Averaging (SWA) [1]. Even if you have already trained your model, it’s easy to realize the benefits of SWA by running SWA for a small number of epochs starting with a pre-trained model.
August 11, 2020
Efficient PyTorch I/O library for Large Datasets, Many Files, Many GPUs
Data sets are growing bigger every day and GPUs are getting faster. This means there are more data sets for deep learning researchers and engineers to train and validate their models.
July 28, 2020
PyTorch 1.6 released w/ Native AMP Support, Microsoft joins as maintainers for Windows
Today, we’re announcing the availability of PyTorch 1.6, along with updated domain libraries. We are also excited to announce the team at Microsoft is now maintaining Windows builds and binaries and will also be supporting the community on GitHub as well as the PyTorch Windows discussion forums.
July 28, 2020
PyTorch feature classification changes
Traditionally features in PyTorch were classified as either stable or experimental with an implicit third option of testing bleeding edge features by building master or through installing nightly builds (available via prebuilt whls). This has, in a few cases, caused some confusion around the level of readiness, commitment to the feature and backward compatibility that can be expected from a user perspective. Moving forward, we’d like to better classify the 3 types of features as well as defin...
July 28, 2020
Microsoft becomes maintainer of the Windows version of PyTorch
Along with the PyTorch 1.6 release, we are excited to announce that Microsoft has expanded its participation in the PyTorch community and will be responsible for the development and maintenance of the PyTorch build for Windows.
July 28, 2020
Introducing native PyTorch automatic mixed precision for faster training on NVIDIA GPUs
Most deep learning frameworks, including PyTorch, train with 32-bit floating point (FP32) arithmetic by default. However this is not essential to achieve full accuracy for many deep learning models. In 2017, NVIDIA researchers developed a methodology for mixed-precision training, which combined
May 05, 2020
Updates & Improvements to PyTorch Tutorials
PyTorch.org provides researchers and developers with documentation, installation instructions, latest news, community projects, tutorials, and more. Today, we are introducing usability and content improvements including tutorials in additional categories, a new recipe format for quickly referencing common topics, sorting using tags, and an updated homepage.
April 21, 2020
PyTorch library updates including new model serving library
Along with the PyTorch 1.5 release, we are announcing new libraries for high-performance PyTorch model serving and tight integration with TorchElastic and Kubernetes. Additionally, we are releasing updated packages for torch_xla (Google Cloud TPUs), torchaudio, torchvision, and torchtext. All of these new libraries and enhanced capabilities are available today and accompany all of the core features released ...
